Journal of Dairy Science
Volume 92, Issue 1 , Pages 1-15 , January 2009

Invited review: Assessing experimental designs for research conducted on commercial dairies

Received 30 May 2008 ,Accepted 25 August 2008.

References 

  1. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed.. Hillsdale, NJ: Erlbaum; 1988;
  2. Cox DF. Design and analysis in nutritional and physiological experimentation. J. Dairy Sci. 1980;63:313–321
  3. Dong MC, Van Vleck LD, Wiggans GR. Effect of relationships on estimation of variance components with an animal model and restricted maximum likelihood. J. Dairy Sci. 1988;71:3047–3052
  4. Falconer DS. Introduction to Quantitative Genetics. 3rd ed.. New York, NY: Longman Scientific and Technical; 1986;
  5. Gianola D, Foulley JL. Sire evaluation for ordered categorical data with a threshold model. Genet. Sel. Evol. 1983;15:201–223
  6. Gill JL. Design and Analysis of Experiments in the Animal and Medical Sciences. Ames: Iowa State University Press; 1978;
  7. Goldberger AS. Best linear unbiased prediction in the generalized linear regression model. J. Am. Stat. Assoc. 1962;57:369–375
  8. Henderson CR. Estimation of variance and covariance components. Biometrics. 1953;9:226–252
  9. Henderson CR, Kempthorne O, Searle SR, Vonkrosigk CM. The estimation of environmental and genetic trends from records subject to culling. Biometrics. 1959;15:192–218
  10. Iles MM. What can genome-wide association studies tell us about the genetics of common disease?. PLoS Genet. 2008;4:e33
  11. Jensen J. Genetic evaluation of dairy cattle using test-day models. J. Dairy Sci. 2001;84:2803–2812
  12. Johnson DH. The many faces of replication. Crop Sci. 2006;46:2486–2491
  13. Kononoff PJ, Hanford KJ. Estimating statistical power of mixed models used in dairy nutrition experiments. J. Dairy Sci. 2006;89:3968–3971
  14. Littell RC, Milliken GA, Stroup WW, Wolfinger RD, Schabenberger O. SAS for Mixed Models. 2nd ed.. Cary, NC: SAS Institute Inc; 2006;
  15. McLean RA, Sanders WL, Stroup WW. A unified approach to mixed linear models. Am. Stat. 1991;45:54–64
  16. Milliken GA, Johnson DE. Analysis of Messy. Data Designed Experiments. Volume I. Belmont, CA: Wadsworth Inc; 1984;
  17. Nordlund KV, Cook NB. Commingling dairy cows: Pen moves, stocking density, and health. In: Pages 36–42 in The American Association of Bovine Practitioners Proceedings. Stillwater, OK: Frontier Printers; 2006;
  18. O’Brien RG, Lohr VI. Power analysis for univariate linear models: The SAS system makes it easy. In: Pages 840–846 in Proceedings of the Ninth Annual Conference, Cary, NC. Cary, NC.: SAS Users’ Group International, SAS Institute Inc; 1984;
  19. Oleggini GH, Ely LO, Smith JW. Effect of region and herd size on dairy herd performance parameters. J. Dairy Sci. 2001;84:1044–1050
  20. Ott RL, Longnecker M. An Introduction to Statistical Methods and Data Analysis. 5th ed.. Pacific Grove, CA: Duxbury; 2001;
  21. Ravagnolo O, Misztal I. Effect of heat stress on nonreturn rate in Holsteins: Fixed-model analyses. J. Dairy Sci. 2002;85:3101–3106
  22. Riley RD, Abrams KR, Lambert PC, Sutton AJ, Thompson JR. An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes. Stat. Med. 2007;26:78–97
  23. St-Pierre NR. Invited review: Integrating quantitative findings from multiple studies using mixed model methodology. J. Dairy Sci. 2001;84:741–755
  24. St-Pierre NR. Design and analysis of pen studies in the animal sciences. J. Dairy Sci. 2007;90:E87–E99
  25. St-Pierre NR, Jones LR. Interpretation and design of nonregulatory on-farm feeding trials. J. Anim. Sci. 1999;77:177–182
  26. Stevenson JS, Pursley JR, Garverick HA, Fricke PM, Kesler DJ, Ottobre JS, et al. Treatment of cycling and noncycling lactating dairy cows with progesterone during ovsynch. J. Dairy Sci. 2006;89:2567–2578
  27. Stroup WW. Power analysis based on spatial effects mixed models: A tool for comparing design and analysis strategies in the presence of spatial variability. J. Agric. Biol. Environ. Stat. 2002;7:491–511
  28. Swanson KS. Using genomic biology to study liver metabolism. J. Anim. Physiol. Anim. Nutr. (Berl.). 2008;92:246–252
  29. Tempelman RJ. Generalized linear mixed models in dairy cattle breeding. J. Dairy Sci. 1998;81:1428–1444
  30. Tempelman RJ. Experimental design and statistical methods for classical and bioequivalence hypothesis testing with an application to dairy nutrition studies. J. Anim. Sci. 2004;82:E162–E172
  31. Tempelman, R. J., and G. J. M. Rosa. 2004. Empirical Bayes approaches to mixed model inference in quantitative genetics. Pages 149–176 in Genetic Analysis of Complex Traits Using Sas. A. M. Saxton, ed. SAS Institute Inc., Cary, NC.
  32. Thompson R, Meyer K. A review of theoretical aspects in the estimation of breeding values for multi-trait selection. Livest. Prod. Sci. 1986;15:299–313
  33. Van Tassell CP, Smith TP, Matukumalli LK, Taylor JF, Schnabel RD, Lawley CT, et al. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat. Methods. 2008;5:247–252
  34. Van Vleck LD. Selection Index and Introduction to Mixed Model Methods. Boca Raton, FL: CRC Press Inc; 1993;
  35. Van Vleck LD, Cassady JP. Unexpected estimates of variance components with a true model containing genetic competition effects. J. Anim. Sci. 2005;83:68–74
  36. Weigel KA. Controlling inbreeding in modern breeding programs. J. Dairy Sci. 2001;84:E177–E184
  37. Weigel KA, Gianola D, Yandell BS, Keown JF. Identification of factors causing heterogeneous within-herd variance components using a structural model for variances. J. Dairy Sci. 1993;76:1466–1478
  38. White IMS, Thompson R, Brotherstone S. Genetic and environmental smoothing of lactation curves with cubic splines. J. Dairy Sci. 1999;82:632–638

☆ Support from the Michigan Agricultural Experiment Station (Project MICL01822) is gratefully acknowledged.

PII: S0022-0302(09)70304-2

doi: 10.3168/jds.2008-1404

Journal of Dairy Science
Volume 92, Issue 1 , Pages 1-15 , January 2009

Article Tools

* Image Usage & Resolution